5e080d5b-cb4a-426d-bf70-4f7021d8c705 - samples
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ABSTRACT:
PROVIDER: EGAD00001008673 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:This study contains methyl-binding domain sequencing and shallow whole genome sequencing from circulating cell-free DNA (cfDNA) for 143 patients with metastatic cancer of known type, 41 patients with Cancer of Unknown Primary (CUP) and 27 non-cancer controls.
| EGAD00001011178 | EGA
Project description:Small-cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, each with unique therapeutic vulnerabilities. Implementing subtyping in the clinic has remained challenging due to limited tissue availability, particularly for longitudinal monitoring. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that there would be subtype-specific patterns of DNA methylation that could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts of totally 179 SCLC patients and machine learning approaches, we developed a highly accurate DNA methylation-based classifier (SCLC-DMC) that could distinguish SCLC subtypes using clinical tumor samples with 95.8% accuracy in the testing set compared to mRNA-based profiling. We further adjusted the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC) we could demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. Furthermore, methylation-based subtyping predicted response to a wide variety of drugs in preclinical models and clinical outcomes were indistinguishable in cohorts of patients subtyped using mRNA or SCLC-DMC. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and guide precision SCLC therapy.
2024-01-22 | GSE241673 | GEO
Project description:We performed shallow whole genome sequencing (WGS) on circulating free (cf)DNA extracted from plasma or cerebrospinal fluid (CSF), and shallow WGS on the tissue DNA extracted from the biopsy in order to evaluate the correlation between the two biomaterials. After library construction and sequencing (Hiseq3000 or Ion Proton), copy number variations were called with WisecondorX.
2021-11-17 | E-MTAB-10548 | biostudies-arrayexpress
Project description:We exploited six paired pre-treatment and post-chemotherapy circulating tumour cell patient-derived explant (CDX) models from donors with extensive stage SCLC to investigate changes at disease progression after chemotherapy. WES data from the patient germ line and from the biopsy of the solid tumour is reported in this submission and RNASeq data from these models is reported previously (E-MTAB-8465 - RNA of Small Cell Lung Cancer Circulating Tumour Cells Derived Explants). Both chemo naive and progression models were taken from patients and their drug sensitivity tested within the mouse models. Blood samples were taken from patients, and RosetteSep CTC Enrichment was used to implant these cells into mice. Tumours were allowed to grow, then extracted and re-implanted into second and third generations of mice, before finally being subjected to molecular profiling.
2021-09-15 | E-MTAB-10880 | biostudies-arrayexpress
Project description:Objectives: Small cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based biomarker classifier to assist in SCLC diagnoses. Materials and Methods: We profiled deregulated circulating cell-free miRNA in the plasma of SCLC patients. We tested selected miRs on a training cohort and created a classifier by integrating miRNA expression and patient clinical data. Finally, we applied the classifier on a validation dataset. Results: We determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group. Conclusion: This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.
2023-10-31 | GSE240758 | GEO
Project description:Objectives: Small cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based biomarker classifier to assist in SCLC diagnoses. Materials and Methods: We profiled deregulated circulating cell-free miRNA in the plasma of SCLC patients. We tested selected miRs on a training cohort and created a classifier by integrating miRNA expression and patient clinical data. Finally, we applied the classifier on a validation dataset. Results: We determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group. Conclusion: This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.
2023-10-31 | GSE240757 | GEO
Project description:Although Thyroid transcription factor-1 (TTF-1, encoded by NKX2-1 gene) is highly expressed in small cell lung carcinoma (SCLC) and lung adenocarcinoma (LADC), difference in the functional roles of TTF-1 between SCLC and LADC remains to be elucidated. The aim of this study was to clarify the differences in the TTF-1 binding regions and functional roles in SCLC and LADC. Employing chromatin immunoprecipitation-sequencing (ChIP-seq) , here we compared the genome-wide TTF-1-binding profiles and the TTF-1-mediated transcriptional programs in a SCLC and a LADC cell lines. We also investigated ASCL1 binding regions in SCLC cells.
2019-11-30 | GSE129312 | GEO
Project description:Although Thyroid transcription factor-1 (TTF-1, encoded by NKX2-1 gene) is highly expressed in small cell lung carcinoma (SCLC) and lung adenocarcinoma (LADC), difference in the functional roles of TTF-1 between SCLC and LADC remains to be elucidated. The aim of this study was to clarify the differences in the TTF-1 binding regions and functional roles in SCLC and LADC. Employing chromatin immunoprecipitation-sequencing (ChIP-seq) , here we compared the genome-wide TTF-1-binding profiles and the TTF-1-mediated transcriptional programs in a SCLC and a LADC cell lines. We also investigated ASCL1 binding regions in SCLC cells.
2019-11-30 | GSE139868 | GEO
Project description:Small Cell Lung Cancer (SCLC) is the most aggressive type of lung cancer with early metastatic dissemination and invariable development of resistant disease for which no effective treatment is available to date. Mouse models of SCLC based on inactivation of Rb1 and Trp53 developed earlier showed frequent amplifications of two transcription factor genes: Nfib and Mycl. Overexpression of Nfib but not Mycl in SCLC mouse results in an enhanced and altered metastatic profile, and appears to be associated with genomic instability. NFIB promotes tumor heterogeneity with the concomitant expansive growth of poorly differentiated, highly proliferative, and invasive tumor cell populations. Consistent with the mouse data, NFIB expression in high-grade human neuroendocrine carcinomas correlates with advanced stage III/IV disease warranting its further assessment as a potentially valuable progression marker in a clinical setting. Genomic DNA from mouse small cell lung tumor samples was analyzed by mate pair sequencing and low coverage sequencing. And RNA from Nfib overexpressing mouse small cell lung cancer cell lines was further analyzed for high quality RNA profiles using Illumina Hiseq2500. This series contains only RNA-seq data.
2016-07-29 | E-GEOD-82005 | biostudies-arrayexpress
Project description:Methyl-CpG-binding domain (MBD) sequencing of neuroblastoma tumors
| PRJNA285076 | ENA